The impact of lifestyle, measured with the HLPCQ questionnaire on the prevalence of metabolic syndrome in Poland: a multicenter study

Metabolic syndrome is one of the most common health problems for people around the world. The aim of our study was to assess the prevalence of metabolic syndrome among adults without prior diagnosis of cardiovascular disease, diabetes, and chronic kidney disease. We also plan to assess the influence of certain lifestyle components on prevalence of metabolic syndrome. The study involved cardiovascularly healthy patients undergoing lab tests, measurements, and the HLPCQ questionnaire (The Healthy Lifestyle and Personal Control Questionnaire). The data were used to diagnose metabolic syndrome. Out of 1044 patients from 10 primary care facilities, 23.3% met the metabolic syndrome criteria, showing a strong link with increased blood pressure, cholesterol, and fasting glucose. Lower scores in the Organized physical exercise subscale of the HLPCQ questionnaire were noted in those with metabolic syndrome. Comparing the subscale of HLPCQ questionnaire, the lower results in Organized physical exercise subscale were found among the participants with metabolic syndrome, both male and females. Metabolic syndrome, a significant risk factor for cardiovascular disease, should be screened for actively, even in apparently healthy populations. Results obtained in our study from analysis of HLPCQ show that screening for metabolic syndrome should be preceded by prevention based on regular physical activity and proper eating habits.


Materials and methods
Our study was designed as a multicentred, retrospective observational study.It was conducted on the Polish population ≥ 35 y.o.without prior diagnosis of CVD, diabetes and chronic kidney disease, who attended the primary care settings to participate in the prevention program.
The inclusion criteria were as follows: 1. Lack of CVD, diabetes and chronic kidney disease when entering the study 2. Age 35-65 y.o.
3. Written consent to participate in the study 4. Performed laboratory tests within 4 weeks before or after entering the study: blood lipids panel (serum total cholesterol, HDL, LDL, non-HDL, triglycerides) and serum fasting glucose level Patient needed to meet all of 4 inclusion criteria.
The data collection period was between 09.2022 and 05.2023.There were 10 primary care facilities involved in the recruitment process and the data collection.The four of them were located in rural areas and six of them in the cities.
Patients during primary care physician appointment underwent an analysis of medical data: laboratory test (blood lipids panel: serum total cholesterol, HDL, LDL, non-HDL, triglycerides and serum fasting glucose level), age, sex, dwelling place, educational status and professional activity.The information about family history (mother and father) of myocardial infarction and stroke, including the age of disease presentation was collected.Participants were also asked about smoking habits (current smoking, smoking in the past and passive smoking) and levels of physical activity.In the area of physical activity we assessed the intensity and the duration (minutes per week).The medium level of physical activity was defined by activity that causes faster breathing and faster heart rate (i.e.riding a bike at normal pace, playing volleyball or fast walking).The intense physical activity was described as an activity that causes very fast breathing and very fast heart rate (i.e.heavy lifting, aerobic exercises, running, riding a bike at faster pace).The anthropometric measurements were performed (height and weight with BMI calculation, waist circumference) as well as heart rate and blood pressure.
The waist circumference was measured in the point of a half-distance between costal arch and the highest point of iliac crest in the mid-axillary line as stated in the recommendations 1 .
In the last step, the participants were asked to fill the HLPCQ questionnaire.It was firstly introduced in Greece and in 2021 was validated and adapted for the Polish population.The HLPCQ was designed as a 26 question tool to assess the frequency of some lifestyle habits.The stratification was based on Likert 4 degree scale: 1-never or rarely, 2-sometimes; 3-often; 4-always.From 26 questions-12 address dietary choices and habits and 2 evaluate the level of physical activity.There are 4 questions which assess social factors and mental balance and 8 questions in the area of daily routine and time management.
3) Daily routine-questions- The evaluation of the questionnaire is based on total score.The higher number of points correlates with a healthier lifestyle.The maximum score is 104.The assessment in each subscale is also possible 21 .

Metabolic syndrome
The diagnosis of metabolic syndrome was made if obesity (defined by BMI) or abdominal obesity was present and 2 out of 3 additional criteria were fulfilled:

Statistical analysis
The analysed data had quantitative and qualitative characteristics.We used the Shapiro-Wilk test.The assessment of qualitative data was made by the chi 2 test.To analyse the quantitative parameters we used Mann-Whitney U test.Statistical significance was assumed at the level of < 0.05.Subsequently, a complex logistic regression model was built, where the dependent variable was the diagnosis of metabolic syndrome, and the independent variables included gender, age, place of residence by the scores of the HLCPQ scale and its individual subscales.Calculations were performed using Statistical 13 software by TIBCO Software Inc. (Palo Alto, CA, USA).

Informed consent statement
All participants were fully informed about the aims of the study and signed the informed consent form prior to completing the research instruments.Participation was voluntary, and confidentiality and anonymity were safeguarded at all times.

Characteristic of the research group
1044 patients from 10 primary care facilities were enrolled in the study after meeting the inclusion criteria.Median of age was 47.9 ± 9.3 years old The predominance of female gender (64,4%) was found. 1 out of 4 participants were current smokers and 16.9% of the studied group declared smoking in the past.The metabolic syndrome criteria was met by 242 participants (23.3%).The prevalence of metabolic syndrome was higher in rural areas and in participants with low levels of physical activity.Patients with higher educational status and white-collar workers presented with metabolic syndrome less often.
Detailed description is presented in Table 1.

Biochemical test analysis
Comparison analysis showed positive association with presence of metabolic syndrome (both in male and females) and higher systolic, diastolic blood pressure and higher serum total cholesterol, LDL, non-HDL, triglycerides and fasting glucose.The negative association was found with HDL cholesterol.Detailed description is presented in Tables 2 and 3.
Vol:.( 1234567890) Comparing the subscale of HLPCQ questionnaire, the lower results in Organized physical exercise subscale were found among the participants with metabolic syndrome, both male and females.Females with metabolic syndrome presented with lower results in Healthy dietary choices subscales.In the male population the difference was found in Dietary harm avoidance subscales.Detailed description of results from subscales is presented in Tables 4 and 5.A complex logistic regression model showed that among the lifestyle parameters analyzed, after accounting for the influence of age and place of residence, regular physical activity has a significant impact on reducing the risk of developing metabolic syndrome among both men and women.The exact results of the regression analysis are shown in Table 6.

Discussion
In this study we hypothesized that Healthy Dietary Choices, Dietary Harm Avoidance, Daily Routine, Organized Physical Exercise and Social and Mental Balance are associated with the metabolic syndrome occurrence and that this association might differ between males and females.We have found 23.2% prevalence of metabolic syndrome among the population considered as healthy and thus, participating in CVD prevention program.Occurrence of metabolic syndrome was higher among males than females (24.7% vs. 22.3%) and was associated with demographic characteristics of studied participants.We have observed an association between lower level The prevalence of metabolic syndrome varies between geographical regions (sometimes even on provincial levels), genders and ethnic groups [22][23][24][25] .Economic and occupational status are also factors contributing to those differences.The heterogeneity of metabolic syndrome criteria may also influence the statistics 26,27 .Most of the studies are consistent on association between age and increased prevalence of metabolic syndrome correlation 25,[28][29][30][31] .The global prevalence of metabolic syndrome varies from 12.5% to 31.4% based on the definition.It seems to be significantly higher in the Eastern Mediterranean Region and Americas and with the country's level of income 32 .However in the study assessing the metabolic syndrome among children and adolescents, the prevalence of metabolic syndrome was not consistently higher with increasing levels of development 33 .On the other hand in the low income countries association between urbanization and increased prevalence of metabolic syndrome was found 34 .Moreover, the differences in prevalence of metabolic syndrome may vary between urbanized and rural areas within the same region.In the meta-analysis of 35 studies from China, the population living in rural areas had a lower prevalence of metabolic syndrome compared to the urban areas 31.3%).However, data from a Bangladesh study which was conducted mostly in rural areas showed the pooled prevalence of metabolic syndrome at 30% 26 .In view of our study Poland can be placed in the middle of metabolic syndrome prevalence ranges (23.2%) with a higher incidence in residents of rural areas, farmers and individuals with lower levels of www.nature.com/scientificreports/education.Contrary to the results of our study, most of the studies show the tendency of higher prevalence of metabolic syndrome in females 23,27-29,35,36.Some studies showed no statistically significant difference between genders 26,30,31 .Increased body weight and large waist circumference are major risk factors for metabolic syndrome 37 .Physical inactivity was found to be a significant risk factor for metabolic syndrome [38][39][40] .This has been confirmed by numerous studies.For example, a study of postmenopausal women found that physical inactivity is one of the most significant factors that increase the risk of developing metabolic syndrome 11 .In contrast, a study on a group of 532 Chinese women found that an unhealthy lifestyle has a significant impact on the risk of developing metabolic syndrome more strongly expressed in men than in women 41 .Converging observations were made in a study conducted by M. Al Thani, where poor diet, low physical activity and smoking were associated with a twofold increase in the risk of developing metabolic syndrome 42 .The other contributing factors are smoking, high-fat diet, sugar sweetened beverages 39,43 .We should also mention the impact of sleep on the risk of developing metabolic syndrome.It has been proven that both too little and too much sleep affect the increased risk of weight gain and increase in body fat 44 .Among dietary habits, stimulants used should also be mentioned the negative effect of alcohol on weight gain, triglyceride levels and blood pressure, and thus accelerate the development of metabolic syndrome 45 .Some studies point out that lower educational status may be associated with high prevalence of metabolic syndrome, as well as some occupations such as drivers, firefighters and emergency department services clinicians.These, sometimes contrary epidemiological data enlighten the heterogeneity and complexity of metabolic syndrome and CVD risk factors.The importance of early diagnostic and therapeutic intervention seems to be crucial as metabolic syndrome is associated with a twofold increase in cardiovascular outcomes, 1.5-fold increase in all-cause mortality as well as increased risk of type 2 diabetes 23,35,46 .
In a meta-analysis of prospective cohort studies in the elderly population, metabolic syndrome was associated with an increased risk of all-cause and CVD mortality 47 .The other study stressed that female gender in the older population with metabolic syndrome presents higher risk of CV mortality compared to males 48 .In the 13-year prospective study on Mediterranean population-based cohort metabolic syndrome was associated with higher risk of incidence of major cardiovascular event, cardiovascular and all-cause mortality, but was neither associated with higher risk of myocardial infarction or stroke 49 .Interestingly, single components of the metabolic syndrome were associated with a similar magnitude of increased CVD 49 .In white adults, higher waist circumference was positively associated with higher mortality at all levels of BMI from 20 to 50 kg/m2 50 .There are some indications that metabolic syndrome may be a risk factor for stroke recurrence 51 .The correlation between metabolic syndrome and atrial fibrillation was also found 22 .
Mierzecki et al. conducted a series of studies on young people from Poland (19-39 years old), initially considered as healthy adults.However, after assessment of all CVD risk factors it turned out that the analysed population was burdened with excessive body mass (BMI ≥ 25 kg/m2 was found in 38.7% subjects) or dyslipidaemia (71.7% individuals).Moreover significant metabolic differences between males and females were observed, which convinced the researchers that the division to sexes should be maintained during further analysis 52,53 These findings are in agreement with our study where older, but still considered until now as healthy population, was characterized with common occurrence of increased waist measurement, arterial hypertension, dyslipidaemia or hyperglycaemia constituting the metabolic syndrome.Another study from Poland conducted by Kalinowska et al. enrolled patients with schizophrenia (mean age 41.89 ± 9.7 years), thus population burdened with a chronic mental disease.The prevalence of metabolic syndrome in this population was 51%.Such high rate is associated not only with the course of the disease (social withdrawal, lower level of physical activity, poor eating pattern), but also with pharmacotherapy including atypical neuroleptics, of which clozapine and olanzapine have the highest influence of metabolic syndrome development 54 .It can be easily seen that the occurrence of metabolic syndrome in the group of Poles with mental illness is twice as high as in the so-called healthy population analysed in our study.This leads to an assumption that initially the Polish population is characterized with presence of risk factors common for cardiovascular disease and metabolic syndrome.Due to such a high burden, preventive programs and interventions focused on a healthy lifestyle seem justified.
To our knowledge there are no studies evaluating the use of HLPCQ in individuals with metabolic syndrome.Moreover, research on linkage between healthy eating patterns, physical activity and interventions designed to decrease the risk of metabolic syndrome development is scarce.One of significance for this topic is a recent, cross-sectional study from China evaluating the personal and treatment control as well as coherence in 275 participants with high risk of metabolic syndrome development.It was shown that these are indeed key determinants of health behaviour, enabling the design of suitable health interventions 55 .Another significant for our study report, originating from South Korea, scoped on the topic of health promotion behaviours among working adults at risk for metabolic syndrome, which occurrence is quite similar to our studied population (26% vs 23.2%).Five health promotion behaviours were identified: physical exercise, healthy food choices, avoiding fatty foods, eating a nutritious and balanced diet, and eating regular moderate meals.Also in this study, perceived behavioural control was a key predictor or determinant of health behaviours 56 .We have observed that lower levels of physical activity are associated with metabolic syndrome regardless of sex, but in the case of eating pattern-Healthy Dietary Choices were significant for females and Dietary Harm Avoidance for males.The interpretation of this result in our population could be explained by differences in cultural roles of both sexes.The domain of Healthy Dietary Choices in HLPCQ is based on questions regarding active evaluation of quality and quantity of food, calculating calories, checking food labels and preparing food.Thus, the activities that are usually connected to women in Poland.Whereas, the domain of Dietary Harm Avoidance is based on questions regarding omitting fast-foods, sweetened beverages or big portions of food-without requiring active quality assessment or in-depth knowledge about food products.
The authors are aware of the limitation of the present study, which is undoubtedly the lack of representativeness of the study group for Polish society.In addition, there is no data on patients' use of medications other than those used to treat diabetes, cardiovascular disease or chronic kidney disease, which may affect patients' weight.Moreover, our study did not analyse alcohol consumption and sleep assessment, which affect patients' health well-being.Therefore, it is necessary to conduct further studies on a representative group of patients.

Conclusions
As metabolic syndrome is a common health condition, identified as an important risk factor for CVD, it is necessary to actively search for its components even in populations considered as healthy.The results obtained in our study from the HLPCQ analysis indicate that regular physical activity is an important lifestyle factor for reducing the risk of developing metabolic syndrome in both men and women.The preventive non-pharmacological measures need to be addressed from multiple angles including lifestyle, cultural and socioeconomic aspects and often should depend on regional needs rather than on universal approach.

Table 2 .
Comparison of laboratory test results, weight, height, BMI and waist circumference between patients with and without diagnosed metabolic syndrome among men.BMI-body mass index, kg-kilograms, pstatistical significance; M-mean; SD-standard deviation; N-number; mmHg-millimetres of mercury; mg/ dl-milligrams per decilitre; SBP-Systolic Blood Pressure; DBP-Diastolic Blood Pressure, HR-Heart Rate, LDL-low-density lipoprotein; HDL-high-density lipoprotein †Mann-Whitney U test; ‡Chi-squared test; statistically significant values are in bold with the significance level set at p < 0.05.

Table 3 .
Comparison of laboratory test results, weight, height, BMI and waist circumference between patients with and without diagnosed metabolic syndrome among women.BMI-body mass index, kg-kilograms, pstatistical significance; M-mean; SD-standard deviation; N-number; mmHg-millimetres of mercury; mg/ dl-milligrams per decilitre; SBP-Systolic Blood Pressure; DBP-Diastolic Blood Pressure, HR-Heart Rate, LDL-low-density lipoprotein; HDL-high-density lipoprotein †Mann-Whitney U test; ‡Chi-squared test; statistically significant values are in bold with the significance level set at p < 0.05.

Table 4 .
Summary of HLPCQ scale results for the entire group and for people with and without metabolic syndrome among men.M-mean; SD-standard deviation; Mann-Whitney U Test statistically significant values are in bold with the significance level set at p < 0.05.
HLPCQThe whole group M ±

Table 5 .
Summary of HLPCQ scale results for the entire group and for people with and without metabolic syndrome among women.M-mean; SD-standard deviation; Mann-Whitney U Test statistically significant values are in bold with the significance level set at p < 0.05.

Table 6 .
Results of a complex logistic regression model assessing the effect of age, place of residence and individual lifestyle parameters on the risk of developing metabolic syndrome for women and men.OR-odds ratio; CI-confidence interval, significant values are in bold with the significance level set at p < 0.05.